Accurate predictions of a human observer's performance, in terms of not only range at which the human will detect, classify, recognize, or identify a target but also how long it takes to accurately detect, classify, recognize, or identify a target while using electro-optical and forward looking infrared (FLIR) systems are important for several reasons. These predictions are guides for system designers and developers since predictions can be made on theoretical systems prior to manufacture to determine if they are expected to meet performance specifications. The predictions also allow purchasers of systems a way to evaluate manufactured systems for the same purpose. Finally, war game simulations and tactical decision aids use these predictions in order to evaluate engagement tactics. Therefore it is important to accurately model the range and amount of time required for a human to perform a visual discrimination task such as detecting, classifying, recognizing, or identifying a target.
The primary modeling technique for resolution and sensitivity requirements known in the prior art is the Vollmerhausen Targeting Task Performance (TTP) metric. The TTP metric uses several parameters to accurately model the environment, the imaging system (from entrance optics of the imager through the display), and also incorporates a numerical approximation of Barten's eye Contrast Threshold Function (CTF) to represent the human observer. The TTP metric is then multiplied by the angle subtended to the eye of the target and yields the psychophysical quantity of resolvable cycles (V). These resolvable cycles were used to develop an empirical Target Transfer Probability Function (TTPF). The TTPF then predicts the probability of task completion (detection, classification, recognition, identification) for a particular target or target set at various ranges with some confidence.
The original TTP metric was developed to predict a human observer's ability to detect and identify military vehicles at long ranges, >1 km. At this range, a typical advanced tactical sensor with a 50 μm pixel integrated a space of more than 0.2 m2, which was sufficient when discussing large vehicles, >3 m long. The other assumption of a static scene was also sufficient since most vehicles were either static or moving slowly (in angular velocity) into an attack posture. While this worked well for characterizing human visual discrimination performance for identification of large objects that were either stationary or moving slowly at a large range, it is insufficient for objects moving at moderate speeds and close range or objects which may be rotating and thereby changing their profile to the imaging system. When research into modeling the detection and identification of humans and handheld objects commenced, it became apparent that the tactical range had dramatically shifted from kilometers to hundreds of meters. It is predominately this short range that renders the static observer/static target assumption invalid. An observer moving at only 30 mph will cover a 500 m range in less than 1 minute.
To compensate for this limitation most war gamers assume that a target is static and randomly distributed in aspect to the imaging system and the time to make a higher level visual discrimination decision, such as classification or identification, is assumed to be an instantaneous event. The difficulty of an observer to classify or identify a randomly aspect distributed target is the cycle criterion for the TTPF (V50). This V50 is the number of resolvable cycles required to correctly detect, classify, recognize, or identify a target with 50 percent probability. This cycle criterion is reported for a number of different target sets but remains as an average over the predefined aspects a target may assume in the environment. In an attempt to determine the V50 for either a moving imaging system or a non-stationary target, experiments are being conducted using movies, which has led to an order of magnitude increase in the generation and storage of imagery causing monetary resources to be used for computer memory upgrades. The time to make a higher level visual discrimination decision, such as classification or identification, is usually measured but rarely reported.
The primary technique for modeling the temporal response of a human is known in the prior art as the Edwards-Vollmerhausen Time Limited Search (TLS) model. The TLS model uses the known probability of detection for a target given an infinite amount of time (P∞) to predict the actual probability of human observers if they do not have an infinite amount of time to search an image or scene. The cumulative probability of finding a target within time t is calculated using an exponential buildup curve equation. The mean time to detect the target is a calibrated parameter which varies due to scene complexity and image quality, but is a function of P∞. This TLS equation was developed for and strictly applied to the task of detecting targets. Many war gamers assume the task of classifying, recognizing, or identifying targets is an instantaneous event. This assumption could allow for an over prediction in the effectiveness and speed of an engagement involving imaging systems. A human observer may delay in making an identification simply because the target may be moving or turning in hopes of obtaining a better/easier aspect on which to make their decision. This delay in identification affects the temporal speed of a possible engagement and could cascade through an entire war game simulation.
In light of the above, the objectives of the present invention are to provide: a method for predicting the probability of detection, classification, recognition, and identification of either stationary or moving targets with a either a stationary or moving imaging system; a method which can be calibrated from more cost efficient static image experiments; finally a method which can be applied to mono-chrome or gray scale visible spectrum imagery, short-, mid-, and long-wave infrared spectrum imagery.